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What Is The Objective Function Of Svtm

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. Support Vector Machines:
SVM is a state-of-the-art machine learning algorithm which is used in text analysis. They are universal learners. They exist is various forms- linear and non-linear. It uses function called kernel. They are not dependent on the dimensionality of feature space. Using an appropriate kernel, SVM can be used to learn polynomial classifiers, radial basic function(RBF) [35]. The goal of SVM is to find the large margin hyperplane that divides two classes. Equation of hyperplane is wT.x+b where class is yϵ{1,-1} depending on features space is x. If the data is linearly separable, the optimal hyperplane maximizes the margin between the positive and negative classes of the training dataset. This line splits the data into
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There are several benefits of SVM- High Dimension Input space – Since the classifier does not depend on number of features, SVM can handle high dimension space [35]. More importantly, in sentiment analysis, many features are available. Document Vector Space- Text categorization is linearly separable [34]. Most document contain few non-zero elements.
SVM classifiers worked the best in majority of papers. In this project, SVM classifier with linear kernel is used.
3.3.3 Rule Based:
This technique was one of the first few methods used for text classification. Human-created logical rules are applied to categorize the text [39]. Most sentiment analysis use Machine Learning techniques. Rule based method can detect sentiment polarity. The issue with Rule-Based methods is, it is difficult to update and the rules and the rules may not be able to cover every scenario [37].
A rule consists of antecedent and its consequent. It has if-else relation [40]. For instance,
Antecedent => consequent
Antecedent defines the condition of the rule. It can be sequence of tokens or just a token. Multiple tokens or rules are concatenated by the OR operator “^”. This token could be word or proper noun. The target term represents terms within the context of the text such as person, company name, brand name etc. Consequent is the sentiment which could be positive, negative or neutral. It will be the result of the antecedent. The rule looks like:
{happy} => positive
{angry} => negative
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